Household, Bank, and Insurer Exposure to Miami Hurricanes: a flow-of-risk analysis

Finance and Economics Discussion Series

Federal Reserve Board, Washington, D.C.

ISSN 1936-2854 (Print)

ISSN 2767-3898 (Online)

Household, Bank, and Insurer Exposure to Miami Hurricanes: a

ﬂow-of-risk analysis

Benjamin N. Dennis

2023-013

Please cite this paper as:

Dennis, Benjamin N. (2023). “Household, Bank, and Insurer Exposure to Mi-

ami Hurricanes: a ﬂow-of-risk analysis,” Finance and Economics Discussion Se-

ries 2023-013. Washington: Board of Governors of the Federal Reserve System,

https://doi.org/10.17016/FEDS.2023.013.

NOTE: Staﬀ working papers in the Finance and Economics Discussion Series (FEDS) are preliminary

materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth

are those of the authors and do not indicate concurrence by other members of the research staﬀ or the

Board of Governors. References in publications to the Finance and Economics Discussion Series (other than

acknowledgement) should be cleared with the author(s) to protect the tentative character of these papers.

Household, Bank, and Insurer Exposure to

Miami Hurricanes: a ﬂow-of-risk analysis

Benjamin Dennis

∗

Federal Reserve Board

February 7, 2023

Abstract

We analyze possible future ﬁnancial losses in the event of hurricane

damage to Miami residential real estate, where the hurricane’s de-

structiveness reﬂects climate-change. We focus on three scenarios: (i)

a business-as-usual scenario, (ii) a Hurricane-Ian-spillovers scenario,

and (iii) a cautious-markets scenario. We quantify bank exposures

and loss rates, where exposures are proportional to the size of real

estate markets and loss rates depend on post-hurricane devaluations

and insurance coverage. This quantitative methodology could com-

plement modeling of local economy impacts, stress on public ﬁnances,

asset market losses, and other ﬁnancial developments that will also

aﬀect banks.

∗

The views expressed in this paper should not be attributed to the Federal Reserve

Board and are the sole responsibility of the author. The scenario analysis in this paper

is a research eﬀort and is unrelated to the Federal Reserve Board’s recently announced

Pilot Climate Scenario Analysis. This paper builds on contributions by my colleagues in

S&R Policy Planning and Strategy to a review of Miami climate risks, including Joseph

Cox, Jonathan Loritz, Jacy Su, Nick Tabor, Justin Warner, and Aurite Werman. Other

participants in the Miami study included Brian Bailey, Kyle Binder, Saba Haq, Nick

Klagge, Andy Polacek, John Schindler Jr., Solomon Tarlin, Lauren Terschan, and James

Wang. I thank Liz Marshall and Roisin McCord for their review of the code. Jake

Clark was instrumental in applying the Hazus tool. I also thank Benjamin Kay for many

helpful comments. While the insights of these individuals were instrumental in shaping

this analysis, all errors and omissions remain my own.

Contents

1 Introduction 3

2 Analytical approach 6

2.1 Loss absorption by insurers . . . . . . . . . . . . . . . . . . . 7

2.2 Loss absorption by borrowers . . . . . . . . . . . . . . . . . . 8

2.3 Loss absorption by creditors . . . . . . . . . . . . . . . . . . . 9

3 Scenarios 10

3.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.2 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.3 The Business-as-Usual (BAU) Scenario . . . . . . . . . . . . . 14

3.4 The Hurricane Ian Spillover Eﬀects Scenario . . . . . . . . . . 16

3.5 The Cautious Markets Scenario . . . . . . . . . . . . . . . . . 21

4 Conclusion 25

A Model 28

A.1 Homeowner types . . . . . . . . . . . . . . . . . . . . . . . . . 28

A.2 Residential real estate dynamics . . . . . . . . . . . . . . . . . 28

A.3 Equity held by non-homeowners . . . . . . . . . . . . . . . . . 29

A.4 Ensuring consistency between stocks and ﬂows . . . . . . . . . 30

A.5 Average size of mortgage by cohort . . . . . . . . . . . . . . . 31

A.6 Number of homes in each mortgage cohort . . . . . . . . . . . 33

A.7 Solving for mortgage repayments . . . . . . . . . . . . . . . . 34

A.8 Determining equity holdings by banks, securities purchasers,

and GSEs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

B Climate Change Damage Generation Process 36

B.1 Apportioning ﬂood and wind damage . . . . . . . . . . . . . . 36

C Insurance as the ﬁrst loss-absorbing layer 36

C.1 The extensive vs the intensive insurance margin . . . . . . . . 38

D Homeowners as the second loss-absorbing layer 38

D.1 Insurance coverage as a fraction of replacement value vs total

property value . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

D.2 Keeping track of loss cushions by cohort . . . . . . . . . . . . 40

D.3 Default risk by segment and cohort . . . . . . . . . . . . . . . 41

D.3.1 Cohort equity and strategic default . . . . . . . . . . . 41

E Non-homeowner asset holders as the ﬁnal loss-absorbing layer 44

F Data addendum 46

G Basis for scenario assumptions 48

G.1 Hurricane shock devaluation . . . . . . . . . . . . . . . . . . . 49

G.2 Home price depreciation . . . . . . . . . . . . . . . . . . . . . 49

G.3 Housing unit growth . . . . . . . . . . . . . . . . . . . . . . . 50

G.4 Turnover rates . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

1 Introduction

The accelerating impact of climate change has added urgency to eﬀorts to

understand how severe weather events might aﬀect the safety and soundness

of the ﬁnancial system.

This paper takes a bottom up approach by inves-

tigating the impact of sea level rise on the vulnerability of banks to losses

on their residential real estate portfolios in Miami. The deﬁning feature of

this analysis is that it traces what we deﬁne as the ﬂow-of-risk across enti-

ties. Pozsar (2014) [31] introduced the term ﬂow-of-risk as a means of taking

derivatives and other risk shifting mechanisms into account when quantifying

exposure to speciﬁc ﬁnancial risks in the shadow banking sector. We simi-

larly use the term as a means of identifying true exposure to climate risks

taking into account all known loss allocation arrangements. In the event

of a hurricane in Miami, insurance companies take the ﬁrst loss (net of de-

ductables). When insurance coverage does not exist or is insuﬃcient, losses

spill over to homeowners. If homeowners default for whatever reason, losses

accrue to mortgage originators or purchasers depending on their exposure.

Thus, the model shares a kinship with waterfall models developed to un-

derstand loss distributions across asset managers in the wake of the Global

Financial Crisis. This kinship extends to the recognition that institutional

and contractual details matter in the passthrough of losses from one party

See, for example, the most recent Sixth Assessment of the Intergovernmental Panel

on Climate Change (IPCC): https://www.ipcc.ch

to another, and the degree to which losses are contingent on complementary

factors.

The focus on residential real estate is an attractive starting point given

the signiﬁcant role of banks in mortgage lending and the availability of bank

balance sheet data that can be matched against hurricane and ﬂood risks.

While exposure to residential real estates in Miami is admittedly a small

fraction of any large national bank’s portfolio, this exercise provides a tem-

plate that can be extended to other assets and regions. With a suitable

methodology for aggregating across these assets and regions, taking into ac-

count correlations between climate events and spillovers across regions, a

given bank’s total exposure to climate risk can be evaluated.

We choose Miami given that its unique exposure to sea level rise has re-

ceived a lot of interest. The high risk to Miami from sea level rise combined

with its susceptibility to hurricanes imply that homeowners might have a

markedly higher incentive to strategically default on their mortgages in the

event of climate-related losses. There are two recent books that cover real

estate markets in Miami in the context of sea level rise (Ariza 2020 [1], and

Goodell 2017 [20]). Each city-region is distinguished by unique set of physical

and transitional climate risks. Miami is wedged between two large bodies of

water that will overwhelm the city if sea levels rise. Complicating decision-

making, local climate oﬃcials must coordinate across 34 municipalities in

Miami-Dade County alone. In addition, Miami is built on porous limestone

through which water can inﬁltrate. Not only does this allow water to by-

pass seawalls, but saltwater inﬁltration is also degrading Miami’s freshwater

aquifers.

In the ﬂow-of-risk framework, a climate-linked natural disaster leads to

insurable claims for covered homeowners, generating losses for insurers.

the extent that insurance policy premiums accurately reﬂect climate risks and

homeowners are appropriately insured against those risks, insurance should

be suﬃcient to prevent any losses from passing through to banks. Yet not

all households have insurance, especially if they are not required to have

it, and those that do may not be fully insured. This applies both to ﬂood

and wind insurance which are sold by diﬀerent insurance companies in sep-

arate markets (whether wind is bundled into general homeowner’s insurance

The ﬂow-of-risk considerations examined here are a subset of the eﬀects described by

Batten et al. (2016) [5], who provide a comprehensive mapping of direct and indirect

impacts of a climate-linked natural disaster on the ﬁnancial system.

automatically or is required as a separate rider depends on the insurers as-

sessment of wind vulnerability within the policy-holders vicinity). Potential

homeowners looking to take out a mortgage to purchase property located in

FEMA-designated ﬂood plains are often formally required to purchase ﬂood

insurance by lenders. However, there are concerns that lenders may be ex-

empting as many as half of these borrowers from this requirement.

For those

with insurance, coverage may not be suﬃcient to replace the home, especially

if it is now necessary to elevate the home on pilings, construct private water

drainage infrastructure, or build to higher local construction codes. Poli-

cies written to cover the actual depreciated value of structures may fall far

short of the necessary reconstruction funds, and homeowners may often in-

sure themselves to the minimum required level. As discussed in Section 3.1,

property-level data on insurance coverage is limited or unavailable.

Insurance losses only pass through to banks if homeowners do not in turn

absorb uninsured losses. Losses to homeowners include two distinct com-

ponents: losses due to damage to structures, and losses due to devaluation

of the land parcel. For residential mortgages, the historical record on price

devaluation is limited. Many homeowners have beneﬁtted from some form

of insurance or disaster relief and strategic default rates have been low. In

many cases, property values have rebounded from isolated natural disasters

within three to seven years. When authorities have determined that a land

parcel is no longer suitable for habitation, the government has often acted

to buy out homeowners and make them whole.

However, if the number of

at-risk properties becomes too large, governments may not be able to absorb

these losses. A scenario that produces the trifecta of uninsurability, large and

unexpected adaptation costs (such as for condominium owners in the wake of

the Surfside tragedy), and losses too large to be absorbed by the government

could produce a large impact on prices. Although some research has found

an impact of climate change on home prices, it does not seem likely that

prices fully reﬂect climate risks (see, e.g., Dennis, 2022, [9]).

Section (2) describes the ﬂow-of-funds analytical approach, with a de-

scription of how losses are calculated for each loss-absorption layer. Section

(3) describes the data, assumptions, and design of each of our three scenar-

This point, initially raised in informal discussions with insurance experts, is supported

by coverage data from NFIP as described in Section 3.1.

See, e.g., NYT, “U.S. Flood Strategy Shifts to ‘Unavoidable’ Relocation of En-

tire Neighborhoods”: https://www.nytimes.com/2020/08/26/climate/ﬂooding-relocation-

managed-retreat.html

ios, and presents results. Section (4) concludes. Addition details on model

calculations and other information are provided in the appendix.

2 Analytical approach

We model hurricanes at each boundary of the Saﬃr-Simpson Hurricane Wind

Scale – Categories 1 through 5 – that follow a predetermined path through

the heart of Miami-Dade County using the HAZUS FEMA physical mod-

eling tool.[13]

This tool uses the physical characteristics of both a deﬁned

hurricane system and Miami to determine damage rates on a census tract

basis. These damage rates are then applied to properties.

The HAZUS software takes into account various factors such as wind-

speed, track speed, tidal elevation, width of hurricane, etc. to calculate wind

and ﬂood damage in a combined model. The relative balance of ﬂood and

wind damage will depend on many factors, including storm characteristics

in addition to the characteristics of built structures in the storms path (e.g.,

frame vs. masonry, age, height, purpose, etc.). For example, a wide, slow-

moving storm with slower winds will create a high ratio of ﬂood to wind

damage. A narrow, fast-moving Category 5 hurricane will primarily cause

wind damage (although ﬂooding due to storm surge will depend on the to-

pography of the shoreline). These many features are captured by the HAZUS

model.

HAZUS provides a series of wind and ﬂood loss rates as a fraction of

replacement value for single family homes, multifamily dwellings, and mobile

homes by census tract. Replacement values are also available from HAZUS,

allowing us to calculate losses. Actual property values are equal to the re-

placement value plus the value of the land parcel. Although it is generally

the case that the land parcel adds value to the structure, this is not always

the case. Local amenities may have a negative value that lowers the sales

price of a property below the replacement value, especially in areas in which

climate risks are rising sharply.

We then map census tracts to ﬂood zones using FEMA National Risk

Index data and use U.S. Census data to identify areas of high-to-medium

income owners, lower-income owners, and primarily rental properties. These

The boundaries are given by the following maximum sustained wind speeds: Category

1 = 74 mph, Category 2 = 96 mph, Category 3 = 111 mph, Category 4 = 130 mph, and

Category 5 = 157 mph.

distinctions are used to impute the amount of insurance coverage likely to

prevail by census tract. We assume that ﬂoodplain homes are more likely

to carry ﬂood insurance as a condition of securing a mortgage. Evidence

suggests that this coverage is only around 50 percent, however, given that

half of homeowners allow their ﬂood coverage to subsequently lapse.

. We

also assume that lower income households are less insured relative to high-

to-middle income households, again most likely due to lapses in renewing

policies. Analysis of the aftermath of Hurricane Ian will provide much more

information about how well insured Florida households are, and whether

insurance will remain within the reach of most households.

We derive the mortgage exposure of banks to each census tract, and

therefore each of the six homeowner categories, using Home Mortgage Dis-

closure Act (HMDA) data. HMDA mortgage data provides information on

the type of borrower, the location of the property, the loan-to-value ratio,

and many other factors for a wide variety of ﬁnancial institutions. However,

it does not provide the stock of mortgage assets on these institutions? bal-

ance sheets. We get information on the stock of mortgages from Y-14M data.

One beneﬁt of Y-14M data is that it apportions bank-originated mortgages

into mortgages held by banks, mortgages sold to the GSEs, mortgages that

are securitized and sold, and mortgages sold in the interbank market. Bank-

originated mortgages account for only a third of all mortgages, with the ma-

jority of mortgages oﬀered through non-bank ﬁnancial institutions (NBFIs)

such as Rocket Mortgage. Banks tend to keep around half of the mortgages

they originate and sell the rest, and the choice of which mortgages to retain

will reﬂect a risk management strategy. Lacking data on which mortgages are

retained and held on banks’ own books, we assume that mortgage retention

and sales are proportional to the stock of mortgage loans in each of the six

categories. This assumption will overstate the risk to banks (and understate

the risk to purchasers) if banks retain the least risky categories of mortgages

for their portfolios.

2.1 Loss absorption by insurers

The ﬁrst layer of loss absorption is insurance, net of deductible. Because

homeowners insurance deductibles are so small, typically $500 to $2,000, we

There are no comprehensive datasets on insurance coverage of households. We base

our estimates of coverage rates on reporting on the impact of Hurricane Ian, see, e.g.,

Rozsa and Werner (2022) [33], and Flavelle (2022b) [15]

do not model them in our simulations. There are three types of homeowner

policies in the model: (i) National Flood Insurance Program (NFIP), (ii)

private ﬂood insurance, and (iii) homeowners insurance. The ﬁrst two types

of insurance are speciﬁc to ﬂooding, while the latter covers damage by wind.

In the event that wind and ﬂood damage coincide, we assume that homeowner

insurers will insist that the damage be classiﬁed as ﬂood damage.

Most homeowners lack ﬂood insurance. Recent reporting in the wake

of Hurricane Ian suggests that only half of homes in ﬂoodplain areas in

the counties evacuated for Hurricane Ian had ﬂood insurance, while only

20 percent in non-ﬂoodplain areas had ﬂood insurance (see, e.g., Rozsa and

Werner (2022) [33], and Flavelle (2022b) [15]). It is unclear why so many

mortgage borrowers lack ﬂood insurance, which is a requirement of securing

a mortgage in designated ﬂoodplain areas. Most likely ﬂood policies are

allowed to lapse after the original mortgage is secured.

Homeowners insurance, which typically covers wind damage, is available

for the replacement cost of the home’s structure. The combined value of

the home’s structure and the land parcel on which the home sits is equal

to the price of the home. If the land value does not change in the wake

of a hurricane, it is possible for insurance to make the homeowner whole,

which seems to be the historical norm. However, climate change may cause

a climate event to lead to a devaluation of the land parcel in addition to

structural damage to the home. This potential devaluation is most likely

if insurers are led by climate change to withdraw coverage by declining to

renew insurance policies (or to increase premiums beyond the reach of most

homeowners). There is no practical method for insuring land value, so some

homeowner losses are not covered by the insurance loss absorption layer.

Our speciﬁc insurance assumptions are discussed further below.

2.2 Loss absorption by borrowers

Borrowers are next in line to absorb losses not covered by insurers. Borrower

equity will vary by many diﬀerent factors including tenure in the home. We

assume that all borrowers purchase their homes initially with a 30-year ﬁxed

rate mortgage and a 20 percent down payment. We assume that there is

a constant rate at which homes are sold, which allows us to solve for the

cohort distribution where cohorts are deﬁned by the number of years since

purchase. To the extent that uninsured structural damage and land devalua-

tion caused by a hurricane reduces homeowner equity, homeowners may owe

more on their mortgages than their homes are worth. Recent cohorts, who

have newly purchased their homes, will have a large loan-to-value ratio and

will consequently be more likely to default.

The literature on post-natural disaster home values tends to ﬁnd that

home values recover with three to seven years of the disaster implying that

land values are typically durable. Homeowners who expect the eventual

recovery of their home values will be less likely to default, viewing any deval-

uation as temporary. However, for Miami, climate change is likely to perma-

nently devalue properties in harms way through at least three channels. The

ﬁrst is the carrying cost of modifying the property to withstand higher sea

levels and enduring more frequent or damaging storms. The second is the

cost in reduced amenities values caused by eroding public services, higher

taxes, and other community eﬀects of climate-induced changes. The third is

the increased diﬃculty of insuring property, including reduced availability of

insurance or increased premiums.

This implies that studies that tie default rates to negative equity need to

be modiﬁed for land value devaluation in addition to considerations such as

whether the state has non-recourse laws in which the lender cannot go after

more than the collateral of the home itself. Florida is a recourse state. We

use an adaptation from Bhutta et al. (2010) [6], which focuses in part on

Florida default rates, to impose a linear relationship between negative equity

and default probability with an ad hoc adjustment to capture the expectation

of permanent devaluation.

For each cohort of each type of borrower, we then apply the non-default

rate times the homeowner’s loss (both uninsured damage and devaluation)

to determine the amount of loss absorbed by homeowners. Remaining losses

pass through to the next loss absorption layer.

2.3 Loss absorption by creditors

Credit originators include both banks (which originate roughly one-third of

mortgages) and non-bank ﬁnancial institutions (NBFIs). However, banks

only retain a portion of the loans that they originate on their balance sheets.

The remainder are securitized, sold to government-sponsored entities such

as Fannie Mae or Freddie Mac, or sold to other banks. Conﬁdential Home

Mortgage Disclosure Act (HMDA) data can track annual mortgage origina-

tions and sales by homeowner type and location, allowing us to calculate

the exposure of diﬀerent creditors and purchasers to homeowner type and

climate-vulnerable locations. We assume that the share breakdown of these

mortgage ﬂows is equivalent to the balance sheet composition for these insti-

tutions, with some institutions carrying a heavier exposure to climate risk.

For each housing type (single, multi, mobile homes)/homeowner type

(high/middle income, low income, investor)/locational vulnerability/cohort

quad in each census tract (where census tracts diﬀer in the mix of housing

type – single family homes, multi-family homes, and mobile homes – and

home prices), we apply the appropriate default rate (the portion of borrowers

in that category who default) times 80 percent of the net value of outstanding

principle minus the ex-post collateral value of the home. The percentage

represents an assumption that there is a foreclosure friction for the bank of

20 percent of value of the home.

Once we have the total quad losses, we allocate them across creditor

institutions using the share exposures of each institution to each quad. For

example, suppose net default losses for low-income homeowners in mobile

homes in ﬂood-prone census tract X total $100 across all cohorts, and Bank

Y accounts for 50 percent of low-income, mobile home mortgages in this

census tract. We would calculate that Bank Y experiences losses of $50.

3 Scenarios

Scenario design is complicated due to the many diﬀerent exogenous variables

not calculated within the model. This iteration of the ﬂow-of-risk model

takes home price levels and growth rates, turnover rates, interest rates, in-

surance rates, sea level rise, home construction rates, and other factors as

exogenous. These factors interact, however, and so they cannot be chosen

completely independently. Ideally, consistency across exogenous variables

could be enforced through an asset valuation model (or module) that would

complement the ﬂow-of-risk analysis. For example, if insurance premiums

were to rise sharply (or if insurance is no longer available for some homes),

we would expect a “syndrome” to follow in which insurance coverage falls,

home construction slows, home prices decrease, and mortgage interest rates

rise (if for no other reason than a rising risk premium). We also, critically,

assume post-hurricane devaluation rates in which the amenities value (or the

value of the land parcel of the property distinct from the value of the built

structure) falls by a ﬁxed amount. Historically, land parcel devaluation in

the wake of a climate disaster has not played a signiﬁcant role. However,

climate change impacts may lead to permanent revaluations of certain loca-

tions, and we allow for it. At the very least, the scenarios that we develop

need to follow a consistent narrative. With this in mind, we construct four

scenarios to explore how bank losses respond to the following conditions:

• Business-as-Usual (BAU)

• Hurricane Ian Spillover Eﬀects

• Cautious Markets

3.1 Data

First, we discuss the key data sources for our analysis. From the Home Mort-

gage Disclosure Act (HMDA) dataset, we obtain the following data on Mi-

ami mortgages on an institution level basis by census-tract: combined loan-

to-value ratio, whether within limit for conforming loans, lien-status, loan

amount, loan purpose, loan term, loan type, property type, property value,

purchaser type, name of lending institution, whether sold/guaranteed/transfered

to another institution, occupancy, construction method, dwelling category,

debt-to-income ratio of borrower, business/commercial purpose, applicant

income, median census-tract family income, ratio of average tract income to

MSA income (as percentage), number of units in tract, number of owner oc-

cupied units in tract, action type, and applicant race. From Y14M data, we

obtain the names of covered banks, their category (e.g., LISCC, RBO, etc.),

total mortgage holdings, and custodial holdings for GSEs and securities-

holders.

From Elliot et al. (2017) [10], we obtain by neighborhood: the num-

ber of housing units, the number of occupied units, the number of low-

middle income (LMI) households, the number of very-low-income (VLI) plus

(LMI) households, population, the number of households, the number of

white/black/other race residents, the number of hispanic residents, the la-

bor force unemployment rate, the poverty rate, the share of renter-occupied

housing, the share of LMI renter-occupied housing, the share of VLI renter-

occupied housing, the share of owner-occupied housing under $200 thousand,

the share of rental occupied housing where rent is less than $1,000 per month,

the amount of single family housing, the median home value, and whether

housing is ﬂood prone.

We match the ﬂow data from HMDA with the stock data from Y14M

to generate the stock-ﬂow ﬁgures from appendix section (A) in combination

with the following assumptions. Downpayments are assumed to be 20 percent

of the value of the loan in all cases. Low-income households are deﬁned as

having no more than $117 thousand in income.

. Unoccupied or renter-

occupied homes are classiﬁed as other (i.e., “OX”). The number of “LX”

homes is calculated as the share of sub-$200K owner-occupied homes (from

Elliot et al., 2017, [10]) times the number of primary occupancy homes (from

HMDA). The number of “HX” homes the therefore the total of primary

occupancy homes minus “LX” homes. “OX” homes are all non-primary

residence homes (from HMDA).

Home prices are taken from the Zillow Home Value Indices (bottom-,

middle-, and top-tier homes in the Ft. Lauderdale/Miami region) and the

National Association of Realtors (median price of existing one-family homes

for Miami-Ft. Lauderdale and West Palm Beach) as reported in HAVER.

We use an average of top- and middle-tier home values for the price of “HX”

homes, the value of low-tier homes for the price of “LX” homes, and the

NAR median price for “OX” homes.

Initial turnover rates are given by the number of home mortgages from

HMDA divided by existing housing stock net of new home construction

also use data from the American Enterprise Institute on months supply of

existing homes to calculate the turnover as follows: (12/months supply of

housing) * MSA housing inventory * (Miami-Dade County home sales/MSA

home sales).

We use the best 30-year housing market growth rate (for the years 1976-

2005) to project a 2.2 percent annual growth rate for the business-as-usual

(BAU) scenario described below. We use the methodology described in sec-

tion (G.2) to generate price trends for ﬂoodplain homes. We assume ﬂood

plain homes grow at the rate of population growth (1.16 percent), and stop

growing thereafter. We do not model the destruction of housing stock. How-

ever, as described above, we do assume that ﬂoodplain homes lose 80 percent

of their value in the wake of a severe hurricane.

Miami-Dade deﬁnes low-income households as $73.1 thousand in income for a family

of four, or 80 percent of AMI as of April 2020)

While this neglects cash-only sales, this is the turnover rate that is relevant for ﬁnancial

institutions’ ﬁnancial health.

Available on Haver at US Regional ⇒ Selected Regional Indicators ⇒ Housing Market

Statistics.

We use newspaper reporting on ﬂood insurance coverage in the wake of

Hurricane Ian to condition our scenario insurance assumptions (e.g., Flavelle,

2022b, [15]). Home replacement cost values are taken from the Hazus model.

See appendix section F for alternative calculations for both the NFIP ﬂood

coverage rate and replacement cost values.

3.2 Assumptions

Each scenario will share some common parameter values as shown in Table

1. We assume that both investors and owner-occupiers purchase their homes

with mortgages with a 20 percent downpayment.

Banks’ share of mortgage

originations is 34 percent.

If a home is foreclosed, the unrecoverable costs

of foreclosure are 20 percent.

Lastly, the nominal interest rate is 1.5 percent

throughout the period of analysis.

Table 1: Universal parameter values

Investor downpayment rate 0.2

Owner-occupier downpayment rate 0.2

Bank share of mortgage originations 0.34

Foreclosure recovery share 0.8

Nominal interest rate 1.5

We now discuss each scenario in turn. For more information on the choices

for scenario parameters, see appendix section G.

Although a downpayment rate of 20 percent is ideal, the typical downpayment for

ﬁrst-time homebuyers was 7 percent in 2021 and 17 percent for repeat buyers accord-

ing to the National Association of Realtors. https://www.nar.realtor/blogs/economists-

outlook/tackling-home-ﬁnancing-and-down-payment-misconceptions . Our assumption of

20 percent is therefore conservative.

According to 2021 Home Mortgage Disclosure Act (HMDA) data, non-depository,

independent mortgage companies accounted for 63.9 percent of ﬁrst lien, 1-4 family, site-

built, owner-occupied, closed-end home-purchase loans, an increase from 60.7 percent in

2020. https://www.consumerﬁnance.gov/data-research/hmda/summary-of-2021-data-on-

mortgage-lending/

Frame, 2010, [17], e.g., surveys foreclosure discount estimates that range from 22 to

50 percent. Pennington-Cross, 2006, [29] links the discount to loan size, time in real-

estate-owned (REO) status, local house price movements, and being located in a judicial

foreclosure state.

3.3 The Business-as-Usual (BAU) Scenario

We simply extrapolate current growth rates, turnover rates, home construc-

tion rates, interest rates, etc. We also set post-hurricane devaluation rates

at their highest levels given that major hurricane damage was not expected.

While this may represent the expectations of many (most?) home buyers in

Florida, the BAU assumption is also unrealistic in light of climate change.

This scenario may plausibly represent losses in distant future years out to

2050 only if Florida is enormously lucky in avoiding hurricanes between now

and then.

The BAU scenario assumes rapid annual growth in both prices and home

units at growth rates of 2.2 and 1.6 percent, respectively. Properties in both

ﬂood and above-ﬂood plain areas experience similar price and construction

trajectories. In addition, historical turnover rates continue unabated. We

use the estimated share of homeowners within and outside of ﬂood plains

that had ﬂood insurance to proxy for well- and poorly-insured homes (See

Rozsa and Werner, 2022, [33], Flavelle, 2022b, [15].)

Table 2: Scenario – Business as Usual

Floodplain Homes Above-ﬂoodplain Homes

Devaluation

Cat 3 0.4 0.3

Cat 4 0.6 0.4

Cat 5 0.8 0.5

Price Growth

Through 2035 0.22 0.22

2040 plus 0.22 0.22

Unit Growth

Through 2035 0.16 0.16

2040 plus 0.16 0.16

Turnover Rate Change

Through 2035 0 0

2040 plus 0 0

Well-insured share 0.5 0.2

Additive propensity to default 0.4

We develop insurance proﬁles for well- and poorly insured homes for each

of our six diﬀerent homeowner types, given in Table 3. Beyond the higher

insurance coverage of well-insured homeowners, who have NFIP ﬂood insur-

ance, high-income and investor properties in ﬂoodplains are assumed to have

additional private ﬂood insurance given that NFIP policies are capped at

$250,000. Well insured homes’ wind coverage is 80 percent of replacement

cost of the structure of the home (where the cost of the structure is provided

in HAZUS data), while that of poorly-insured homes covers only 55 percent

of the replacement cost.

Table 3: BAU-Scenario Insurance Assumptions

Type Well-insured? NFIP Private Flood Private Wind

(y/n) ($K) ($K) (% structure val.)

HA y 233 0 0.80

HF y 233 40 0.80

LA y 233 0 0.80

LF y 233 0 0.80

OA y 233 0 0.80

OF y 233 40 0.80

HA n 0 0 0.55

HF n 0 0 0.55

LA n 0 0 0.55

LF n 0 0 0.55

OA n 0 0 0.55

OF n 0 0 0.55

Notes: Homeowner types are: HA = high/mid income, above-ﬂoodplain, HF = high/mid

income, ﬂoodplain, LA = low income, above-ﬂoodplain, LF = low income, ﬂoodplain, OA

= other, above-ﬂoodplain, and OF = other, ﬂoodplain.

Simulation results are given in Table 4. We calculate losses in billions

of dollars resulting from a single incidence of the relevant simulated for the

years 2025, 2030, 2035, 2040, 2045, and 2050. The local economy evolves

over time according to each scenario’s assumptions in ways that may make it

more or less vulnerable, depending on the scenario. These losses are reported

for each loss-absorbing agent for each category on the Saﬃr-Simpson scale

from Cat 1 to Cat 5. Although we can disaggregate the data extensively,

we report category totals as well as the share of the banking sector’s Miami-

Dade mortgage portfolio that is lost. It is important to note that these

damage estimates are for a speciﬁcally-parameterized hurricane and should

not be viewed as representative of all possible hurricanes. For example, a

moderately fast and narrow Cat 1 hurricane will cause far less damage than

a wide and slow-moving Cat 1 hurricane.

All parties suﬀer signiﬁcant loss from Cat 3 or higher hurricanes. For a

Cat 5 hurricane that strikes in 2050, the losses for insurers could rise to as

much as $89 billion, with losses to banks on mortgages held on their balance

sheet of $ 6.3 billion (or 54.8 percent of their Miami portfolio).

Given

that Miami-area mortgages constitute a small fraction of the overall asset

portfolio of large banks, these losses in isolation are not likely to threaten

bank solvency. But for smaller banks that are more heavily concentrated in

the area, there might be signiﬁcant distress. The exposure of the various

creditors to our six diﬀerent types of homeowners diﬀers, and HMDA data

allow us to draw distinctions between creditors based on relative exposure to

the most risky households. Loss rates for other holders of bank-originated

mortgages are 28.7 percent for GSE’s, 19.7 percent for securitized mortgage

purchasers, and 19.3 percent for interbank purchasers.

3.4 The Hurricane Ian Spillover Eﬀects Scenario

The next scenario postulates a strong reaction to Hurricane Ian that dramat-

ically alters real estate and insurance markets in Florida.

The continued

existence of insurance as an initial loss buﬀer (after deductables) is question-

RMS Moody’s best estimate of private insurance losses from Hurricane Ian

is $67 billion, with an additional $10 billion loss to NFIP for a total of $77

billion. https://www.rms.com/newsroom/press-releases/press-detail/2022-10-07/rms-

estimates-us67-billion-in-insured-losses-from-hurricane-ian .

We ignore the possibility of ‘put-back’ risk, or the potential that purchasers of se-

curitized mortgages might have a contractual right to return mortgages that fall below

a performance standard to the banks that sold the mortgage. We also assume that the

homeowner-type shares of the mortgages sold by banks matches the distribution of these

shares held on the banks’ own balance sheets. In other words, we do not allow for the

adverse selection or moral hazard that has been investigated by [21] (for securities) and

[27] for GSE purchases.

See, e.g., Flavelle (2022a) [14] which describes potential consequences of out-of-reach

insurance for Florida’s housing market.

Table 4: Scenario – Business as Usual

2025 2030 2035 2040 2045 2050

Cat 1

Insurers 1,422,105 1,540,548 1,668,856 1,807,850 1,958,420 2,121,532

Homeowners 71,806 78,293 90,530 107,396 126,787 150,556

Bank held 24,326 29,734 36,311 43,856 53,261 64,442

% of mortage portfolio 0.6% 0.6% 0.6% 0.6% 0.6% 0.6%

Bank-originated but not held 37,661 46,035 56,241 67,904 82,477 99,792

Bank-originated other 25,564 31,249 38,177 46,094 55,986 67,741

Non-bank originated 169,951 207,741 253,769 306,423 372,170 450,304

Total 1,751,413 1,933,600 2,143,884 2,379,524 2,649,102 2,954,366

Cat 2

Insurers 3,707,877 4,016,694 4,351,233 4,713,635 5,106,219 5,531,502

Homeowners 226,653 249,923 275,385 308,828 342,464 389,847

Bank held 24,326 29,734 36,311 43,856 53,261 64,442

% of mortage portfolio 0.6% 0.6% 0.6% 0.6% 0.6% 0.6%

Bank-originated but not held 37,661 46,035 56,241 67,904 82,477 99,792

Bank-originated other 25,564 31,249 38,177 46,094 55,986 67,741

Non-bank originated 169,951 207,741 253,769 306,424 372,171 450,304

Total 4,192,032 4,581,376 5,011,115 5,486,742 6,012,578 6,603,627

Cat 3

Insurers 7,871,837 8,527,459 9,237,687 10,007,066 10,840,525 11,705,182

Homeowners 19,300,000 23,300,000 28,100,000 33,900,000 40,900,000 49,300,000

Bank held 151,784 181,464 217,225 260,555 312,840 384,027

% of mortage portfolio 3.5% 3.4% 3.4% 3.3% 3.3% 3.4%

Bank-originated but not held 127,474 152,663 183,073 219,409 263,595 332,123

Bank-originated other 95,795 114,684 137,482 164,778 197,927 247,924

Non-bank originated 728,044 871,220 1,043,926 1,251,558 1,503,173 1,871,438

Total 28,274,934 33,147,490 38,919,393 45,803,366 54,018,060 63,840,694

Cat 4

Insurers 20,037,181 21,715,237 23,499,792 25,391,391 27,590,618 29,898,111

Homeowners 28,200,000 33,900,000 40,800,000 49,100,000 59,100,000 71,200,000

Bank held 871,819 1,057,617 1,258,331 1,497,623 1,785,934 2,132,540

% of mortage portfolio 20.2% 20.0% 19.5% 19.2% 18.9% 18.6%

Bank-originated but not held 628,287 756,040 897,038 1,064,928 1,267,365 1,510,590

Bank-originated other 489,774 590,572 701,090 832,728 991,412 1,182,083

Non-bank originated 3,862,709 4,667,033 5,544,892 6,590,835 7,851,498 9,366,589

Total 54,089,771 62,686,499 72,701,144 84,477,505 98,586,827 115,289,912

Cat 5

Insurers 59,675,523 64,673,429 69,979,489 75,794,383 82,118,846 88,953,676

Homeowners 38,700,000 46,200,000 55,300,000 66,200,000 79,400,000 95,200,000

Bank held 2,658,525 3,129,476 3,753,645 4,454,419 5,281,918 6,271,536

% of mortage portfolio 61.5% 59.2% 58.2% 57.2% 55.8% 54.8%

Bank-originated but not held 2,001,947 2,341,945 2,769,197 3,252,756 3,824,587 4,507,738

Bank-originated other 1,548,189 1,813,848 2,151,897 2,533,719 2,984,721 3,523,673

Non-bank originated 12,100,000 14,100,000 16,800,000 19,900,000 23,500,000 27,800,000

Total 116,684,185 132,258,697 150,754,229 172,135,277 197,110,073 226,256,623

able. In the wake of Hurricane Andrew, some insurers went bankrupt, and

several withdrew from the Florida market altogether (see, e.g., MGI 2020

[25]). The state set up a Florida taxpayer-backed supplemental insurance

vehicle to ﬁll the gap, and private captive re-insurance companies emerged

in the Cayman Islands to backstop Florida insurers after more well-known

re-insurers increased rates or left the market. It is possible that private insur-

ance options may cease to exist or that new insurance company entrants are

unreliable. Moreover, the patchwork regulation of insurance by local, state,

and federal regulators implies diﬀerent outcomes by region that challenge a

one-size-ﬁts-all model to metro region ﬂow-of-risk analysis.

We therefore model a stark reaction to Hurricane Ian in which insur-

ers ﬂee the state causing insurance coverage to decline sharply. The lack

of insurability causes home price trends to turn negative and brings new

home construction to a halt for ﬂoodplain housing. Table 5 displays our

assumptions. We lower the ex-post devaluation for ﬂoodplain homes from

the BAU scenario given that homeowners are already aware of the potential

for hurricane damage. Prices for ﬂoodplain homes begin to fall at half the

rate of recent increases through 2035, after which climate change leads them

to fall even more sharply. For above ﬂoodplain homes, prices rise at half

their previous rate through 2035, after which they plateau. Home construc-

tion in ﬂoodplain zones ceases (beyond replacement) while construction in

above-ﬂoodplain zones proceeds at half its previous rate. Greater diﬃculty

in selling ﬂoodplain homes leads to a decrease in the turnover rate that inten-

siﬁes slightly in 2040 and beyond. The well-insured share of both homeowner

types drops severely, and homeowners are more likely to default for a given

loss of equity.

We also make adjustments to insurance coverage for well- and poorly-

insured homeowners. Well-insured now means the homeowner in all six cate-

gories is covered by NFIP ﬂood insurance up to $233,000, and wind damage of

up to 60 percent of the value of the structure. For poorly-insured homeown-

ers, there is no ﬂood insurance coverage (NFIP or private) and homeowners

insurance only covers 30 percent of wind damage.

Simulation results are given in Table 7. In this scenario, mortgage portfo-

lios are much smaller given lower turnover rates and smaller mortgages (due

to reductions in home prices over time). So the loss rates are applied to

smaller balances. In this way, the reaction to Hurricane Ian can be seen as

corrective, helping to right-size the real estate market relative to climate risks.

For example, bank-held mortgages in this scenario reach only $7.4 billion by

Table 5: Scenario – Hurricane Ian Spillovers

Floodplain Homes Above-ﬂoodplain Homes

Devaluation

Cat 3 0.2 0.10

Cat 4 0.3 0.15

Cat 5 0.4 0.20

Price Growth

Through 2035 -0.014 0.11

2040 plus -0.080 0.00

Unit Growth

Through 2035 0 0.008

2040 plus 0 0.008

Turnover Rate Change

Through 2035 -0.2 0

2040 plus -0.3 0

Well-insured share 0.25 0.10

Additive propensity to default 0.55

Table 6: Hurricane Ian Spillovers Insurance Assumptions

Type Well-insured? NFIP Private Flood Private Wind

(y/n) ($K) ($K) (% structure val.)

HA y 233 0 0.6

HF y 233 0 0.6

LA y 233 0 0.6

LF y 233 0 0.6

OA y 233 0 0.6

OF y 233 0 0.6

HA n 0 0 0.3

HF n 0 0 0.3

LA n 0 0 0.3

LF n 0 0 0.3

OA n 0 0 0.3

OF n 0 0 0.3

Notes: Homeowner types are: HA = high/mid income, above-ﬂoodplain, HF = high/mid

income, ﬂoodplain, LA = low income, above-ﬂoodplain, LF = low income, ﬂoodplain, OA

= other, above-ﬂoodplain, and OF = other, ﬂoodplain.

2050 compared with $11.5 billion in the BAU scenario. The distribution of

those mortgages is also more skewed towards non-ﬂoodplain properties in the

Hurricane Ian spillover scenario.

Consequently, instantaneous losses are smaller for all parties, despite the

relative high rates of default and lack of insurance. Again, focusing on a Cat

5 hurricane that hits in 2050, total losses are $63.3 billion in this scenario

compared with $226.3 billion in the BAU scenario. Insurer losses fall from

$95.2 billion to $31.8 billion, and bank-held mortgage losses fall from $6.3

billion (54.8 percent of the Miami mortgage portfolio) to $2.2 billion (or

29.5 percent of the portfolio). Milder hurricane scenarios due lead to higher

losses under this scenario than the BAU scenario, however. The assumed

deterioration in price trends and turnover rates beginning after 2035 lead to

higher percentage losses on banks’ mortgage portfolios under the Hurricane

Ian Spillover scenario for Cat 1 through 3 hurricanes from 2040 onwards. In

a sinking real estate market, moderate shocks will lead to higher rates of

default. However, once shocks become suﬃciently large, the sinking-market-

fragility eﬀect is overwhelmed by the generally poor level of resilience of the

entire market.

3.5 The Cautious Markets Scenario

Our ﬁnal scenario envisions real estate markets turning cautious while in-

surance coverage rates rise signiﬁcantly. Agents take maximum action to

anticipate and prepare for climate risks under this scenario, with speciﬁc as-

sumptions given in Table 8. In many aspects, the parameter assumptions

are similar to those of the Hurricane Ian Spillovers scenario. The main dif-

ferences are that almost all households, regardless of ﬂoodplain status, are

well-insured; that even prices for above-ﬂoodplain homes eventually begin to

decline, and that propensity to default is lower given that expectations are

better calibrated towards climate risks.

The deﬁnition of well-insured now means 90% coverage of structural dam-

age, full NFIP insurance, and an additional $40,000 of private ﬂood insur-

ance, as shown in Table 9. Poorly-insured is almost identical except for the

absence of ﬂood insurance.

Simulation results are given in Table 10. Given that insurers now absorb

the bulk of losses, loss rates fall for all other parties. Even so, insurer losses

are roughly comparable to the BAU losses and are even lower in extreme

cases, such as a Cat 5 hurricane in 2050, despite the far higher insurance

Table 7: Scenario – Hurricane Ian Spillovers Eﬀect

2025 2030 2035 2040 2045 2050

Cat 1

Insurers 684,771 697,443 697,443 697,443 697,443 697,443

Homeowners 110,766 113,524 119,671 129,330 139,392 151,368

Bank held 51,053 55,810 58,355 58,480 151,444 249,823

% of mortage portfolio 0.8% 0.7% 0.7% 0.7% 2.0% 3.4%

Bank-originated but not held 64,971 72,138 76,427 77,942 144,086 213,125

Bank-originated other 42,141 46,886 49,760 50,894 98,952 149,601

Non-bank originated 307,027 339,385 358,229 363,613 765,759 1,189,066

Total 1,260,730 1,325,187 1,359,886 1,377,703 1,997,076 2,650,426

Cat 2

Insurers 1,867,320 1,903,494 1,903,494 1,903,494 1,903,494 1,903,494

Homeowners 363,299 375,052 380,256 391,229 399,174 416,154

Bank held 51,053 55,811 58,355 60,202 175,490 293,667

% of mortage portfolio 0.8% 0.7% 0.7% 0.8% 2.3% 4.0%

Bank-originated but not held 64,972 72,139 76,428 79,073 160,323 242,720

Bank-originated other 42,141 46,886 49,760 51,786 111,214 171,957

Non-bank originated 307,029 339,386 358,231 370,885 867,759 1,375,022

Total 2,695,814 2,792,768 2,826,524 2,856,671 3,617,454 4,403,015

Cat 3

Insurers 4,034,063 4,116,560 4,116,560 4,116,560 4,116,560 4,116,560

Homeowners 7,370,094 7,459,375 7,437,682 6,470,597 5,828,364 5,406,228

Bank held 53,633 58,976 62,276 302,479 605,080 748,601

% of mortage portfolio 0.8% 0.8% 0.8% 3.8% 7.8% 10.1%

Bank-originated but not held 66,777 74,341 79,144 248,691 461,308 566,296

Bank-originated other 43,646 48,720 51,987 175,705 331,205 408,183

Non-bank originated 318,463 353,368 375,438 1,410,992 2,712,975 3,344,802

Total 11,886,676 12,111,341 12,123,087 12,725,024 14,055,492 14,590,670

Cat 4

Insurers 10,362,955 10,623,717 10,623,717 10,623,717 10,623,717 10,623,717

Homeowners 12,400,000 12,600,000 12,600,000 11,100,000 10,100,000 9,500,216

Bank held 217,369 244,060 251,394 820,514 1,198,659 1,266,701

% of mortage portfolio 3.2% 3.3% 3.2% 10.4% 15.5% 17.1%

Bank-originated but not held 186,410 208,434 215,371 618,625 885,641 933,088

Bank-originated other 130,698 146,923 152,227 445,024 639,499 675,121

Non-bank originated 1,037,514 1,163,575 1,201,573 3,657,495 5,287,376 5,580,707

Total 24,334,946 24,986,709 25,044,282 27,265,375 28,734,892 28,579,549

Cat 5

Insurers 31,028,955 31,834,483 31,834,483 31,834,483 31,834,483 31,834,483

Homeowners 20,200,000 20,500,000 20,400,000 18,500,000 17,200,000 16,200,000

Bank held 1,073,418 1,253,443 1,321,399 2,168,634 2,387,930 2,187,986

% of mortage portfolio 15.8% 16.8% 16.8% 27.5% 30.9% 29.5%

Bank-originated but not held 940,160 1,073,457 1,115,956 1,743,196 1,899,883 1,751,174

Bank-originated other 652,266 747,185 778,090 1,224,745 1,341,413 1,241,419

Non-bank originated 5,174,873 5,967,342 6,241,746 9,970,999 10,900,000 10,100,000

Total 59,069,671 61,375,911 61,691,674 65,442,057 65,563,710 63,315,062

Table 8: Scenario – Cautious Markets

Floodplain Homes Above-ﬂoodplain Homes

Devaluation

Cat 3 0.2 0.10

Cat 4 0.3 0.15

Cat 5 0.4 0.20

Price Growth

Through 2035 -0.02 0.00

2040 plus -0.08 -0.02

Unit Growth

Through 2035 0 0.008

2040 plus 0 0.008

Turnover Rate Change

Through 2035 -0.3 0

2040 plus -0.3 0

Well-insured share 0.9 0.9

Additive propensity to default 0.4

Table 9: Cautious Markets Insurance Assumptions

Type Well-insured? NFIP Private Flood Private Wind

(y/n) ($K) ($K) (% structure val.)

HA y 250 40 0.9

HF y 250 40 0.9

LA y 250 40 0.9

LF y 250 40 0.9

OA y 250 40 0.9

OF y 250 40 0.9

HA n 0 0 0.9

HF n 0 0 0.9

LA n 0 0 0.9

LF n 0 0 0.9

OA n 0 0 0.9

OF n 0 0 0.9

Notes: Homeowner types are: HA = high/mid income, above-ﬂoodplain, HF = high/mid

income, ﬂoodplain, LA = low income, above-ﬂoodplain, LF = low income, ﬂoodplain, OA

= other, above-ﬂoodplain, and OF = other, ﬂoodplain.

coverage rates. This is due to a smaller amount of home construction in

ﬂoodplain areas over our study period. Banks hold far smaller real estate

portfolios, with a combined total of $5.9 billion in mortgage loans held in

2050, compared to $7.4 billion and $11.5 billion in the IAN and BAU scenarios

respectively. Loss rates for banks are much smaller, with maximum losses

of 19.3% in the 2050 Cat 5 outcome, compared with 29.5% and 54.8% in

the IAN and BAU scenarios respectively. Total losses under the Cautious

Markets scenario amount to $98.9 billion, compared with $63.3 billion and

$226.3 billion in the IAN and BAU scenarios respectively.

4 Conclusion

This paper simulates a ﬂow-of-risk approach to a speciﬁc climate event that

aﬀects strategic mortgage defaults. Its value lies in the various considerations

that aﬀect insurance, homeowner, and creditor decisions in the presence of

diversity in borrower charactistics and climate risk. However, even in the nar-

row conﬁnes of the exercise, this ﬂow-of-risk model does not consider market,

counterparty, or operational risks, nor does it endogenously model real econ-

omy impacts. Moreover, even though hurricanes of diﬀerent categories are

considered under conditions of a rising sea level, there are in principle an

inﬁnite number of, say, Category 5 hurricanes that could be designed that

diﬀer by track speed, width, shear, and other characteristics. Each of these

theoretical hurricanes could lead to diﬀerent levels of damages and loss. For

these reasons, the ﬂow-of-risk model should be thought of as a module in

a larger suite of models that could help evaluate climate risk to ﬁnancial

institutions. We now turn to several possible paths forward.

As mentioned, Miami mortgages are likely to be a limited portion of any

large bank’s balance sheet implying that even momentous losses in Miami are

manageable in isolation. However, the correlation of climate events across

both time and space is rising signiﬁcantly.

If a given bank faces repeated

climate disasters aﬀecting a portion of its portfolio, or simultaneous climate

events across all regions of its business footprint or asset types, losses might

add up suﬃcient to threaten distress. One approach might be to repeat-

edly conduct joint ﬂow-of-risk-type analyses across a bank’s major business

See, for example, the increase in both the number and the joint oc-

currences of large climate disasters in NOAA’s Billion Dollar Disasters data.

https://www.ncei.noaa.gov/access/billions/mapping

Table 10: Scenario – Cautious Markets

2025 2030 2035 2040 2045 2050

Cat 1

Insurers 3,054,115 3,128,427 3,128,427 3,128,427 3,128,427 3,128,427

Homeowners 16,189 16,604 17,365 18,606 19,905 21,474

Bank held 50,973 54,387 55,017 52,522 115,595 179,885

% of mortage portfolio 0.7% 0.7% 0.7% 0.7% 1.7% 3.0%

Bank-originated but not held 63,943 69,217 70,812 68,686 107,556 146,145

Bank-originated other 41,463 44,966 46,068 44,776 73,083 101,529

Non-bank originated 303,559 327,226 333,682 322,204 575,042 829,967

Total 3,530,242 3,640,828 3,651,371 3,635,221 4,019,607 4,407,428

Cat 2

Insurers 6,188,516 6,328,672 6,328,672 6,328,672 6,328,672 6,328,672

Homeowners 54,480 56,093 56,760 58,178 59,236 61,449

Bank held 50,973 54,387 55,017 52,611 118,437 185,168

% of mortage portfolio 0.7% 0.7% 0.7% 0.7% 1.8% 3.1%

Bank-originated but not held 63,943 69,217 70,812 68,738 109,278 149,338

Bank-originated other 41,463 44,966 46,068 44,814 74,369 103,918

Non-bank originated 303,559 327,226 333,683 322,551 586,398 851,058

Total 6,702,934 6,880,561 6,891,011 6,875,563 7,276,388 7,679,603

Cat 3

Insurers 11,785,069 12,051,615 12,051,615 12,051,615 12,051,615 12,051,615

Homeowners 6,288,613 6,092,540 5,808,625 4,636,502 3,785,778 3,157,120

Bank held 50,992 54,420 55,076 191,105 374,898 456,171

% of mortage portfolio 0.7% 0.7% 0.7% 2.7% 5.6% 7.7%

Bank-originated but not held 63,954 69,236 70,847 157,592 273,523 326,250

Bank-originated other 41,471 44,980 46,093 108,466 192,494 231,269

Non-bank originated 303,633 327,353 333,914 887,434 1,632,365 1,967,752

Total 18,533,732 18,640,144 18,366,170 18,032,714 18,310,673 18,190,177

Cat 4

Insurers 28,232,298 28,888,386 28,888,386 28,888,386 28,888,386 28,888,386

Homeowners 9,676,562 9,388,947 8,962,466 7,200,111 5,919,813 4,972,503

Bank held 116,142 120,848 116,936 421,167 656,459 729,795

% of mortage portfolio 1.7% 1.6% 1.6% 5.9% 9.9% 12.3%

Bank-originated but not held 105,926 111,977 110,612 305,499 454,178 497,932

Bank-originated other 71,438 75,567 74,582 214,570 322,714 355,708

Non-bank originated 569,747 598,642 586,487 1,827,106 2,782,389 3,073,727

Total 38,772,112 39,184,366 38,739,468 38,856,839 39,023,939 38,518,051

Cat 5

Insurers 82,381,354 84,440,942 84,440,942 84,440,942 84,440,942 84,440,942

Homeowners 13,500,000 13,100,000 12,500,000 10,200,000 8,453,299 7,176,015

Bank held 336,820 394,137 388,143 885,749 1,173,052 1,144,842

% of mortage portfolio 4.9% 5.3% 5.2% 12.3% 17.7% 19.3%

Bank-originated but not held 250,641 291,023 288,556 611,340 794,921 774,158

Bank-originated other 173,863 202,510 200,789 431,884 564,813 551,489

Non-bank originated 1,477,863 1,723,123 1,703,357 3,744,476 4,916,584 4,795,656

Total 98,120,540 100,151,734 99,521,786 100,314,390 100,343,610 98,883,102

regions for diﬀerent climate event severities and correlations supported by

climate modeling. The output could be used to develop a probability dis-

tribution of losses. Similar to stress-testing methodologies, a focus on a

pre-speciﬁed level of tail risk might be used to judge safety and soundness.

In addition, while the scenario inputs are chosen to be plausible, a better

approach would be to derive them from a companion model that integrates

regional economic outcomes with home prices, incomes, and other key vari-

ables. For example, we have modeled the default decision as strategic (based

on the willingness of borrowers to repay their debts). However, even willing

borrowers will not be able to repay if they lose their jobs and are unable make

their payments. A companion model that provides estimates of the impact

on incomes can factor in how changes in the ability to pay might change

default rates. It would also be useful to include expected public support,

which is currently absent from the model. A more holistic approach would

also address market, counterparty, and operational risks in addition to the

credit-risk outcomes addressed by the ﬂow-of-risk model.

Appendix

A Model

To set up the modeling framework, we ﬁrst establish the ﬂow and stock re-

lationships for the adding, ﬁnancing, and distributing of real estate equity.

Prices and price trends are taken as exogenous to the model. Two important

distributional concerns are tackled here. First, homeowners are separated

into six categories reﬂecting income, purpose of homeownership, and expo-

sure to ﬂood risk. Second, homeowners are divided into cohorts that reﬂect

the amount still owed on mortgages relative to the value of the original loan.

Both of these factors will inﬂuence the decision to default in the event of

hurricane damage.

A.1 Homeowner types

We will exploit the homeowner type to distinguish between high- and low-

income residents, and outside investors. We will also distinguish between

homes built inside and outside high-risk ﬂood plains. This in turn gives

six separate types of homeowners. Let the ﬁrst character denote identity

(H=high-income, L=low-income, O=outside investor) and the second char-

acter denote location (F=ﬂoodplain, A=above ﬂoodplain). Thus, we have:

j ∈ {HF, LF, OF, HA, LA, OA} .

We do not count low income households who rent as part of the “LF, LA”

category, as they do not hold mortgages. Properties that are rented to

low-income households are assumed to be part of the high-income, “OF,

OA”, category, whereby the owner will presumably have engaged in the same

investor-motivated behavior that characterizes outside owners.

A.2 Residential real estate dynamics

Homeowner exposure to real estate is determined by the rate at which the

homeowners’ home equity grows. Home equity, E, grows with:

1. The degree of price appreciation realized by (all) homeowners of type j,

which is γ

·H

t−1

·p

t−1

, where γ

is price appreciation between time t−1

and t of the average home of homeowner type j, H

t−1

is the number of

properties of type j, and p

t−1

is the t − 1 price of the average home of

type j;

2. The routine payment of mortgage principal based on the book value

of the home at the time of purchase, which is π

· H

t−1

· p

t−1

, where

reﬂects a steady-state relationship calibrated to represent principal

payments relative to the overall level of the housing stock at time t−1,

exclusive of prepayment;

3. The deacquisition of homes by selling existing properties, which is −ω

t−1

· p

t−1

, where ω

is the rate of turnover of homes of type j between

times t − 1 and t;

4. The prepayment of mortgage debt, which we will associate with turnover

of properties, given by ˆπ

·ω

·H

t−1

·p

t−1

, where ˆπ

is a steady-state ad-

justment factor that calibrates prepayments to the home’s book value;

5. Home acquisition as homeowners of type j purchase existing housing

stock, which is ψ

· ω

· H

t−1

· p

t−1

, where ψ

is the rate of downpayment

as a fraction of the home’s value;

6. Purchase of new housing stock, which is ψ

· ∆H

· p

, where ∆H

represents the increase in the housing stock between times t − 1 and t.

Putting this together, home equity changes according to:

∆E

j=1



+ π

− (1 − ˆπ

− ψ

)ω



t−1

+ ψ

∆H



where E

represents housing equity owned by all homeowners at time t.

A.3 Equity held by non-homeowners

Changes in home equity not held by homeowners, Q

, is equal to:

∆Q

j=1

∆





− ∆E

The amount of this home equity initially held by banks is given by the

following elements:

1. Reductions based on the payment of mortgage principal based on the

book value of the home at the time of purchase as described above,

which is −π

· H

t−1

· p

t−1

;

2. Reductions based on the prepayment of mortgage debt, as described

above, which is −ˆπ

· ω

· H

t−1

· p

t−1

;

3. The ﬁnancing of home purchases, both existing and new, equal to:

(1 − ψ

) · (ω

t−1

· p

t−1

+ ∆H

· p

), where ω

is the rate of turnover of

homes of type j between times t − 1 and t;

Thus:

∆Q

j=1











(1 − ψ

)(ω

t−1

+ ∆H

)

| {z }

New mortgages

− (π

+ ˆπ

t−1

| {z }

Repayments











j=1

k=1



− o



where the k superscript refers to lender type, m

is new mortgages, and o

is repayments.

A.4 Ensuring consistency between stocks and ﬂows

In order to make the parameterization as tractable as possible, we will assume

that the shares of ﬁnancing across homeowner types and lender types are

stable over time. Thus:

= $

where j is homeowner type and k is lender type, and $ is the appropriate

ﬁxed share value and

= 1, ∀ jk ∈ J ×K.

We will also assume that the distribution of originations remains ﬁxed

such that:

= χ

where χ is another ﬁxed share such that

= 1, ∀ k ∈ K.

This implies that repayments are given by:

= χ

− $

∆Q

Summing over k:

= m

− $

∆Q

where $

We can then solve for:

(π

+ ˆπ

)

| {z }

Unknown

− $

∆Q

t−1

| {z }

Known

. (A.1)

The right hand side of this equation is composed of known variables. In

order to address the left hand side of the equation, we take the following

approach. We determine the amount of mortgage prepayment (the second

term on the RHS), by making use of the turnover rate, the average length of

a mortgage, the historical interest rate, and the average historical value of a

mortgage. More speciﬁcally, we determine the average amount of mortgage

remaining for each cohort of each homeowner type, the number of homes

for each cohort-homeowner dyad, and ﬁnally the amount of prepayment due

to the selling of existing homes by homeowner type. The method described

below does not include prepayment for other motives, such as reﬁnancing at

a lower interest rate, which could in principal be included in equation (A.1).

A.5 Average size of mortgage by cohort

Let M

be the amount remaining on mortage j, with M

equal to the original

loan amount and the subscript 0 referring to the ﬁrst year of the mortgage.

For simplicity, we assume that interest is compounded annually. At the end

of the ﬁrst year, the amount owed will be:

= M

(1 + r

) − F

where F

is the ﬁxed (annual) payment (interest and principal) on the mort-

gage, and r

is the contractual interest rate on the mortgage.

Likewise, in the second period:

= M

(1 + r

) − F

= M

(1 + r

)

− F

(1 + r

) − F

and so on. In general, if M

is the original value of the mortgage, than at

any time t:

= M



1 + r



− F

t−1

i=0

(1 + r

)

. (A.2)

where T is the total number of years of the original mortgage (e.g., 30 years).

We can determine F

as a function of the initial mortgage amount and in-

terest rate by noting that at the end of the life of the mortgage (at time T ),

the principal has to be equal to zero, i.e., M

= 0. Thus:

(1 + r

)

T −1

i=0

(1 + r

)

The amount of principal at any given time 1 can be calculated as:

= P

− (F

− M

where P

is principal remaining at time t. At time 2, we have:

= P

− (F

− M

= P

− 2F

+ r

+ M

In general, we will have:

= P

− t · F

+ r

t−1

i=0

. (A.3)

The average mortgage for each homeowner type j is calculated from

HMDA data. We impose this mortgage on all homeowners of type j. We

assume that there is a constant probability (equal to the turnover rate for

homeowner of type j) that any homeowner cohort sells their home in any

given time t. This will then drive the size distribution of cohorts of homes

with mortgages and those that have been paid oﬀ. We then take the value of

average outstanding mortgage principal for each cohort times the amount of

homes remaining in each cohort and multiply it times the turnover rate to get

the overall principal repayment for that cohort in a given year t. Summing

over cohorts in j gives us

. This allows us to solve for the one remaining

free variable in equation (A.1), which is π

A.6 Number of homes in each mortgage cohort

Let us deﬁne H

j,s

as the number of homes owned by homeowners of type j

at time t who took out mortgages s number of years ago. From the HMDA

data, we know the average loan term for homeowners of type j and assume

this is constant. Furthermore, we assume that the turnover rate of home

ownership, ω

is the same across all cohorts. Homeownership will then be

distributed across cohorts as:

s=1

r=t−s

j,s

(1 − ω

)

t−r

+ H

j,0

t−1

(1 − ω

). (A.4)

where S is the loan term of the typical mortgage for homeowner type j (e.g.,

S = 30 if the typical mortgage was a 30-year loan), H

j,s

is the original amount

of households taking out mortgages at time t = s, the number of cohort s

households of type j at time t will be equal to H

j,s

= H

j,s

(1 − ω

)

t−s

, and

j,0

are homes owned by homeowners of type j that are fully owned. The

amount of homes funded by mortgages taken out s years ago is given by total

loans to homeowners of type j found in the HMDA data. We impute turnover

rates from HAVER data provided by Zillow, although we cannot determine

these rates by year. Finally, we know the total amount of homes held by

homeowers of type j at time t. Using this information, we can calculate

j,0

t−1

Let A

j,s

be equal to the average mortgage taken out by a homeowner of

type j who bought a home t − s years ago, which we assume to be equal to

(1 − ψ

t−s

. Using equations (A.2) and (A.3), the amount of mortgage

principal remaining at any given time t will be equal to:

j,s

= 1 − (t − s)

j,s

+ r

j,s

(

t−s−1

i=0



1 + r

j,s



−

j,s

i−1

k=0



1 + r

j,s



##)

where F

j,s

is purely a function of the contractual interest rate:

j,s



1 + r

j,s



T −1

i=0



1 + r

j,s



Let:

j,s

= F



j,s



A.7 Solving for mortgage repayments

We can now write our expression for prepayments due to home sales.

Some fraction of each cohort of each homeowner type, equal to the turnover

rate at time t, will sell their home and retire (prepay) their mortgage. To

determine the amount of total repayments by homeowner type, we need to:

(i) determine the contemporary number of borrowers in each homeowner

type-cohort dyad, (ii) apply the appropriate (time-sensitive) turnover rate

to determine the number of homeowners retiring their mortgages, and (iii)

calculate and aggregage the amount of principal left on the mortgages by

homeowner type.

Consider the case of a single homeowner type and set aside the j super-

script. Designate the amount of homeowners in the present cohort s = t = 0

as H

. The one-period-earlier cohort s = −1 will have a total size equal

to H

−1

(1 − ω

−1

) at time t, where ω

−1

is the turnover rate of the prior pe-

riod. Likewise, the remaining number of cohort s = −2 households will

be given by H

−2

(1 − ω

−2

)(1 − ω

−1

). In general, the amount of cohort s

remaining at the beginning of time t will be given by: H

t−1

r=s

(1 − ω

The share of this cohort that sells their home will be given by the present

turnover rate and will equal: ω

t−1

r=s

(1 − ω

). The value of the mortgages

that they prepay will be equal to the share of principal left to repay times

the value of the original mortgage times the remaining size of the cohort,

t−1

r=s

(1 − ω

). Summing across cohorts gives us the total amount

of prepayment:

s=1

t−1

r=s

(1 − ω

Finally, we acknowledge the diﬀerent homeowner types j to get:

ˆπ

s=1

j,s

t−1

r=s

(1 − ω

)

t−1

. (A.5)

where the prepayment rate is calibrated to the current value of type-j home-

owner housing stock.

We can solve for repayments in two ways. We can calculate repayments

directly using the mortgage rates and principal owed by each homeowner

type and cohort, or we can use the following relationship:

− $

∆Q

t−1

− ˆπ

. (A.6)

There is a continuum of choices for the turnover rate ω

that lead to

corresponding repayment rates π

in equation (A.6). In principle, a unique

combination of repayment rate and turnover rate can be calibrated to mort-

gage income reported on the income statement, but this is beyond the scope

of the paper. Rather, we use information on historical turnover rates, and

consider the future path of turnover rates one of the key scenario choices of

the modeler.

A.8 Determining equity holdings by banks, securities

purchasers, and GSEs

Although these mortgages initially sit with banks, banks will securitize and

sell mortgages on to other parties. These shares are obtained for ﬂows from

HMDA data and Y14M data provide custodial holdings of GSE and securi-

tized mortgages by LISCC institutions for stocks.

We can therefore represent the change in home equity held by investment

funds and other parties as:

∆Q

= ∆B

+ ∆G

+ ∆F

+ ∆NBF I

= B

+ G

+ F

+ NBF I

where B

is bank holdings of home equity, G

is GSE holdings of bank-

originated mortages, F

is investment fund holdings of bank-originated mort-

gates, and NBF I

is holdings of all non-bank ﬁnancial institution (NBFI)

originated mortgages. Note that NBFI-originated mortgages account for the

majority of new mortgages (as high as two-thirds).

Note: Detailed historical data on home sales are provided by Miami-Dade Oﬃce of

Appraisal - See bbs.miamidade.gov.

https://www.wsj.com/articles/nonbank-lenders-are-dominating-the-mortgage-

market-11624367460

B Climate Change Damage Generation Pro-

cess

For each scenario, we model ﬁve hurricanes at the boundary of each Saﬃr-

Simpson category using the FEMA Hazus tool. We choose a track that carries

the hurricanes through the main business district using the “near wave surge

model” approach.

B.1 Apportioning ﬂood and wind damage

The total amount of structural damage that a homeowner can experience is

limited to the replacement cost value of the unit structure. In many cases the

sum of ﬂood and wind damage exceeds the replacement value. It is common

to hear stories of homeowners struggling to get ﬂood and wind insurers to pay

claims because each has determined that the primary damage was inﬂicted

by the condition that they do not insure (e.g., ﬂood insurers insist that the

damage was caused by wind, whereas homeowners insurers insist that the

damage was caused by ﬂooding).

We assume that ﬂood insurance stands

ﬁrst in line, such that if ﬂood damage alone is equal to the replacement cost

of the property, wind damage is equal to zero. In general, wind damage will

be limited to the diﬀerence between the replacement value and ﬂood damage

if wind and ﬂood damage together exceed the replacement cost value.

C Insurance as the ﬁrst loss-absorbing layer

The degree to which insurance absorbs risk depends on the nature of the dam-

age (ﬂood or wind, as described above) and the number of households with

coverage (the extensive margin) and the extent to which those households

are insured (the intensive margin). These two margins will vary signiﬁcantly

across our six diﬀerent household categories. We therefore adopt a two-step

procedure in which the ﬁrst step is to model the speciﬁc type of loss (either

actualized or anticipated), followed by determining the size of the loss and

the amount that would be covered by insurance.

See, e.g., https://www.nytimes.com/2021/09/10/your-money/ida-ﬂood-damage-

insurance-policy.html.

In general, the analysis will determine each entity’s exposure, and then

apply loss rates. These loss rates will be dependent on an ordering of pri-

ority in claims on the underlying asset. The ﬁrst losses will be borne by

insurers up to the limits of their obligations (or their resources). Any losses

above these amounts will spill over to homeowners. Whether homeowners

will completely absorb remaining losses will depend on the share of owner-

ship of the home’s equity, and their ability and willingness to continue to

honor mortgage obligations. Under circumstances in which they cannot or

do not honor these obligations and default, losses will spill over to banks, in-

vestment funds, and GSEs in proportion to their share of the mortgage pool.

This pari passu assumption may be incorrect if, say, securitization contracts

allocate ﬁrst losses to diﬀerent tranches of blended assets (circa 2008 CDOs)

or require the securitizer to take ﬁrst losses.

Insurance coverage for a shock occurring between times t − 1 and t will

equal:

r∈N,F,W



j,r

I,Z,t

· R

j,r



(C.1)

where λ

j,r

I,Z,t

is Type r insurers’ loss rate as a share of total exposure for a shock

of Type Z at time t of coverage of homeowners of Type j (covered in detail

in section (E)); and R

j,r

is exposure of insurers of Type r to homeowners of

Type j.

In matrix notation:

= L

I,Z,t

× R

I,t

where I

is a j × 1 vector given by:













I,Z,t

is a j × 3 · j matrix of insurance sector loss rates applicable to each

Congressional Budget Oﬃce. (2019) [8]

of our 3 · j combinations:

I,Z,t







HF,N

I,Z,t

, λ

HF,F

I,Z,t

, λ

HF,W

I,Z,t

0, 0, 0 · · · 0, 0, 0

0 · · · 0

LF,N

I,Z,t

, λ

LF,F

I,Z,t

, λ

LF,W

I,Z,t

· · · 0 · · · 0

0 · · · 0 0 · · · 0 · · ·

OA,N

I,Z,t

, λ

OA,F

I,Z,t

, λ

OA,W

I,Z,t







and R

I,t

is a 3 · j × 1 vector given by:

I,t







HF,N

I,t

HF,F

I,t

HF,W

I,t

OA,N

I,t

OA,F

I,t

OA,W

I,t







We discuss the determination of the λ terms below.

C.1 The extensive vs the intensive insurance margin

For the extensive insurance margin, or the number of households that have

ﬂood insurance, we assume diﬀering NFIP ﬂood insurance coverage rates for

occupied ﬂoodzone residential units and above-ﬂoodplain units (see scenario

assumptions). This essentially doubles our homeowner categories as we now

have well- and poorly-insured versions of each of our six homeowner clas-

siﬁcations. In what follows, the subscript j therefore refers to 12 diﬀerent

homeowner types: the original six as NFIP-insured, and the original six as

non-NFIP-insured.

D Homeowners as the second loss-absorbing

layer

Any losses that are not covered by insurance spill over to homeowners. Home-

owners must decide whether to absorb these losses in full, or to default on

their mortgages. The default choice depends on both the ability of home-

owners to service their mortgages (or to reschedule), which has not yet been

added to the model, and the willingness to continue servicing their debt (the

strategic default motive).

D.1 Insurance coverage as a fraction of replacement

value vs total property value

Insurance coverage is calibrated to the cost of replacing the structure of

the home, which can be either greater or less than the value of the property

itself. In expensive real estate markets, the cost of the structure is often small

relative to the cost of the land parcel. On the other hand, in declining cities,

the cost of rebuilding a structure might be many times greater than the Zillow

price of the home. If we diﬀerentiate between the land parcel cost and the

replacement cost of the structure, it becomes apparent that climate change

can damage either or both of these categories. A climate event might reduce

the desirability of a parcel of land, leading to a permanent reduction in value

even if the structure on that parcel has not experienced damage directly. This

kind of loss is uninsurable through homeowner insurance riders for ﬂood and

wind damage. Alternatively, a climate event might damage the structure of

the home without reducing the desirability of the land parcel itself, leading

to a loss in value that is insurable. Of course, a climate event may cause

both eﬀects simultaneously. We therefore conﬁne insurance coverage to the

replacement value of the home even as we allow climate “damage” to pass

through to home prices via a reduction in the desirability of a property.

Denote the damage accruing to each household by ˜s

· v

· H

, where v

is the replacement value of the home, and ˜s

is the damage rate as a share

of home replacement value. Clearly, homeowners will experience no loss on

the damage to built property so long as:

= ˜s

· v

· H

This equation holds with equality because insurance payouts will never ex-

ceed the amount of damage. However, it is possible for ˜s

to exceed unity

if, for example, remediation to better protect the property against future

climate events is required (by, say, raising the property up on pilings).

Homeowner losses due to property damages are therefore equal to:

Losses to homeowners of type j =



˜s

· v

· H

− I

, for ˜s

· v

· H

> I

0, for ˜s

· v

· H

= I

To these losses must be added any reductions in the market value of the

land parcel:

Losses due to land parcel devaluation = ∆p

= ˆs

= f(S

where S

is a climate forcing process described below. It is clear that a

good proxy for how much residential real estate prices might decline due to

unanticipated local developments is needed.

D.2 Keeping track of loss cushions by cohort

Because each cohort will have diﬀerent amounts of equity at risk, it is likely

that diﬀerent cohorts of homeowners of the same type j will reach the thresh-

old that triggers default at diﬀerent levels of sustained damage. In general,

we would expect default rates to be a function of: (i) home price appreciation

since purchasing the home, (ii) the turnover rate, and (iii) the contractual

mortgage interest rate, inter alia.

For any given cohort s of homeowner type j, the amount of home equity

held will equal the value of the home minus the principal owed:

j,s

= p

− Υ

j,s

(D.1)

We can therefore include h

j,s

(or its distribution) as an argument in de-

termining the default rate in the wake of a shock.

Equation (D.1) is extremely useful, however, in showing that ﬁnancial sec-

tor vulnerability to mortgage default will depend in part on the composition

of cohorts within a given homeowner type j, the size of the original mortgage

relative the the value of the home (which implicates both price appreciation

since purchase as well as the prevalance of reﬁnancing), and the mortgage

interest rate (with consideration that high interest rates can be reﬁnanced

but also that adjustable rate mortgages may trap less wary borrowers into

higher interest rate payments).

Now that we have established the amount of losses passing through to

homeowners, we focus on the amount of this residual loss that homeowners

There is a large literature on defaults and negative equity that can inform this discus-

sion, including: Bhutta et al. (2010) [6], Scharlemann and Shore (2016) [34], and Foote et

al. (2008) [16].

are willing and able to absorb. The ability to continue to service mortgage

debt will depend on a homeowner’s income and their ability to reschedule

their mortgage. These factors are beyond the homeowner’s control. How-

ever, the homeowner’s willingness to default, that is, the strategic default

motive, is more complicated. To institutional factors that impose penalties

for defaulting (such as whether the state is non-recourse, and the impact on

credit scores) must be added homeowner expectations about whether sharp

(hurricane-induced) home price devaluations will be reversed. The historical

experience is that home prices tend to rebound to their pre-disaster levels

after a few years. Under these circumstances, it makes sense for homeown-

ers to hang onto their homes until prices recover. However, this historical

tendency may not be a good guide for climate change in that sea level rise

or perpetual wildﬁre risk may make such price recoveries impossible. For

example, if the land on which a house is built is permanently submerged due

to sea level rise, home prices will not recover.

Based on the previous section, total damages for the homeowner equal:

= ˜s

+ ˆs

. (D.2)

where %

is the ratio of home price to replacement cost for homeowner of

type j.

D.3 Default risk by segment and cohort

We assume that an increasing fraction χ

of homeowners of type j will walk

away from their mortgages as their losses rise. Let losses per household net

of insurance be deﬁned as:



˜s

− i



| {z }

Structural damage

− ˆs

|{z}

Devaluation

. (D.3)

where i

= I

/(H

D.3.1 Cohort equity and strategic default

Recalling our discussion of cohort equity and equation (D.1), we can write

net (post-event) equity holdings by cohort and segment as:

j,s

= h

j,s

− l

We follow Bhutta et al. (2010) [6] in estimating the share of homeowners

who walk away from their mortgages. Bhutta et al. ﬁnd support for a dual

trigger in which both reductions in the value of the home and reduction in

income inform the decision to default. Climate induced damage will likely

aﬀect both of these variables, but for now we focus solely on the home price

reduction. Florida is a recourse state, meaning that homeowners are still

theoretically liable for mortgage debt even if the home is foreclosed. Fortu-

nately, Bhutta et al. [6] focus on Florida as one of the four states in their

analysis. According to results presented in their Table 5, it takes a reduction

of equity equal to 46 percent of their home’s value for 25 percent of Florida

homeowners to strategically default (ceteris paribus), a reduction equal to 79

percent of their home’s value for 50 percent to strategically default, and a de-

crease in home value equal to 128 percent for all homeowners to strategically

default.

However, there is an external validity concern that we must also address.

In the case in which a negative event leaves the property intact, an individ-

ual homeowner might have some expectation that any decrease in value is

temporary. However, a climate event may leave the property unsuitable for

reconstruction, in which case it is not a question of ‘walking away from your

mortgage,’ but your ‘home walking (ﬂoating) away from you.’ In other words,

strategic default rates are likely to be much higher than those estimated in

Bhutta et al. (2010) [6]

After exploring diﬀerent functional forms, we estimate the percentage

of homeowners who walk away from their mortgages by extracting a linear

relationship based on the California trend from Figure 6 in Bhutta et al. [6]:

j,s

= 0.6 ×



1 −

j,s



+ W

∀





1 −

j,s





< 0.

where as a reminder P

j,s

is the remaining loan balance and p

is the (post-

shock) value of the home. The term W

is a potential adjustment factor to

take into account the unsuitability of reconstruction.

The amount of losses absorbed by homeowners of type j will then be equal

to the share of homeowners who hang onto their homes and fully absorb

losses, plus the share of homeowners who walk away times the amount of

positive equity that was destroyed. Thus, for H

t−1

> 0:

H,Z,t

t−1

s=1



j,s

(1 − χ

j,s

s=1



j,s

t−1

H,Z,t

s=1



j,s

(1 − χ

j,s

)

t−1

| {z }

Stay

s=1



j,s

| {z }

W.A.

where 

j,s

is the share of cohort s households in homeowner type j at time

t, and the abbreviation “W.A.” stands for “walk away.” Note that in the

case that all homeowners walk away, χ

= 1, and the loss rate is also unity,

H,Z,t

= 1. In other words, homeowners of Type j lose all of their equity, but

nothing more. In the case in which no homeowners walk away, homeowners

may absorb losses greater than the amount of their equity, depending on

whether l

> h

In the case in which there is no positive equity, this approach must be

modiﬁed. Homeowners who walk away from negative equity lose nothing

(note that we address the costs of defaulting separately in the determination

of χ

j,s

). The status of those who stay is more complicated. If we adopt a

mark-to-market approach, these homeowners lose an amount equal to the

devaluation of their homes. However, it is not possible to express this as a

‘loss rate’ if equity holdings are negative. As an alternative, we think of the

choice to stay as maintaining an option to beneﬁt from an upside if home

prices were to rise (in addition to the ﬂow of shelter services). The value of

this real option is suﬃciently low as to change very little with the scale of

home devaluation. Consequently, we treat the loss rate λ

H,Z,t

as eﬀectively

zero when E

t−1

< 0.

Deﬁne the following net exposure vector (inclusive of unrealized equity

gains) for homeowners:

E,t







HF I

LF I

OAI

HF U

LF U

OAU







And deﬁne the diagonal matrix of homeowner loss rates as:

E,Z,t







HF I

E,Z,t

· · ·

0 λ

LF I

E,Z,t

· · ·

0 0

· · ·

OAU

E,Z,t







where λ

E,Z,t

= 0 if E

t−1

< 0.

Losses faced by each homeowner group can thus be represented by the

column vector:

= L

E,Z,t

× R

E,t

. (D.4)

E Non-homeowner asset holders as the ﬁnal

loss-absorbing layer

Non-homeowner asset holders experience losses when homeowners default,

and risk-mitigation fails to cover losses.

In this section, we combine our

loss estimates into a framework that apportions losses between banks, GSEs,

and investment funds.

Deﬁne S

as the following vector of dollar damages by homeowner type

The model does not consider losses stemming from mark-to-market concerns leading,

for example, to collateral calls or other disruptive events.







HF I

LF I

OAU







Deﬁne V

as the following (pre-climate event) vector of total home values

for homeowner type j:







HF I

LF I

OAU







The net value of mortgages left to banks, funds, and the government after

deducting unabsorbed losses will then be given by the j × 1 vector:

= (V

t−1

− R

h,t−1

)

| {z }

Orig. net value

− (S

t−1

− N

t−1

)

| {z }

Unabsorb. loss

j=1

s=1

j,s

t−1

| {z }

Collateral val.

, (E.1)

= P

|{z}

Outstanding mort.

−

j=1

s=1

j,s

− V

j,s

t−1

)

| {z }

Mort. defaults net collateral

. (E.2)

where the ﬁrst underbraced term is the value of home equity held by parties

other than the homeowner, and the second term is the spillover loss not

absorbed by homeowners (inclusive of insurance coverage).

We assume that losses are distributed across creditor types on a pari passu

basis with loss shares equal to:

Banks : α





(E.3)

Government : α





(E.4)

Funds : α





(E.5)

NBFI : α

NBF I

= 1 − α

− α

= 1 −



+ G

+ F



.(E.6)

We calculate losses using equation (E.2). We can directly calculate the

principle owed by each homeowner type and cohort, P

j,s

, using the methods

given above and apply our calculations of χ

j,s

to implement the following for

each institution:

Z,t

j=1

s=1

j,s

− V

j,s

t−1

). (E.7)

F Data addendum

We use NFIP data to generate alternative estimates. The number of NFIP

policies per state can be used as a proxy for the number of NFIP policies

per municipality. NFIP policies per state (by ﬂoodzone designation) are

provided by NFIP at: https://nﬁpservices.ﬂoodsmart.gov. The number of

Florida occupied residences located in 100-year and combined 100-to-500 year

ﬂoodplains are provided by FloodZoneData.us [32]. We take the statewide

ratio of NFIP policies written on high risk properties (zones A, AE, AH, and

AO) per residences located in 100-year ﬂoodzones to proxy for the take up

rate of NFIP policies by ﬂoodzone properties at the municipal level. We like-

wise take the statewide ratio of non-ﬂoodzone NFIP policies to non-ﬂoodzone

residences to proxy the take up of above-ﬂoodplain residences. Data on to-

tal occupied residences in Florida is taken from the American Community

Survey (https://data.census.gov), as described in Table 11.

Based on these numbers, 84 percent of Miami’s NFIP policies should be

allocated to ﬂoodzone units, or 292,979 policies. Total occupied ﬂoodzone

units in Miami equal 661,242, for a coverage percentage of 44 percent. The

remaining policies, equaling 53,802 cover a total of 222,931 above-ﬂoodplain

units, for a coverage percentage of 24 percent.

We subtract the number of ﬂoodzone properties from the total number

of occupied units to arrive at above-ﬂoodzone property numbers. We take

the ratio of non-ﬂoodzone NFIP policies to above-ﬂoodzone properties to

determine the coverage rate for above-ﬂoodzone policies in Miami. We use

this ratio in combination with the number of ﬂoodzone residences in Miami

to estimate the number of NFIP policies going to ﬂoodzone residences. We

assume identical takeup rates by HF, LF, and OF homeowners. The excess

of NFIP policies above this estimated ﬁgure is apportioned equally between

above-ﬂoodplain residences.

Table 11: State-of-Florida NFIP Coverage Data

100-year Combined

# of occupied ﬂoodzone units 1,893,920 2,611,010

# of NFIP ﬂoodzone policies 1,041,842 1,041,842

% of ﬂoodzone units NFIP-insured 55.0 39.9

# of occupied above-ﬂoodzone units 6,011,912 5,294,822

# of NFIP above-ﬂoodzone policies 605,767 605,767

% above-ﬂoodzone units NFIP-insured 10.1 11.4

Miami-Dade County

# of NFIP Policies 346,781 346,781

Avg. policy coverage ($ thous.) 233 233

To perform a consistency check on structual replacement values, we used

replacement cost values from Home Construction ProMatcher (https://home-

builders.promatcher.com/cost/miami-ﬂ-home-builders-costs-prices.aspx). Ac-

cording to the surveyed construction ﬁrms in the Miami area, the cost of

custom home building in Miami ranges from $111.35 to $165.33 per square

foot. We assume that the average lower-income home is 1,800 square feet

(22 percent of new single-family homes completed in the South region of the

United States were 1,800 square feet or less according to the US Census 2020

Annual Characteristics of New Housing, which reports data collected by the

US Department of Housing and Urban Development (HUD)) which implies a

replacement cost value of $111.35 × 1,800 ≈ $200,000 for LX homes. Around

65 percent of the total number of homes lie between 1,800 and 3,999 square

feet, so we set HX square footage at 2,600 and take a construction cost of

$134.61 per square foot to arrive at a replacement cost of $350,000. For OX

homes, we take the middle of the cost range, $120, multiplied by the median

home square footage (2,261 in 2020) to get approximately $270,000.

G Basis for scenario assumptions

It is widely recognized that: (i) the underlying stationarity required for the

application of standard quantitative risk assessments – including the deter-

mination of a ‘fundamental’ price or the assumption of normal statistical

moments – is not present with climate change, and (ii) the adjustment of

coastal home prices is highly contingent on beliefs about the reality of cli-

mate change (see, e.g., Pindyck, 2021, [30], Weitzman, 2011, [37], Bakkensen

and Barrage, 2021, [3], and Baldauf et al., 2020, [4]). These factors motivate

us to use a scenario analysis approach to our simulations.

There are four main dynamic housing market variables that diﬀer across

the two scenarios presented here:

1. The degree to which homes suﬀer devaluation in the wake of a hurricane

of a given strength.

2. The trend growth of home prices in the presence of chronic sea level

rise.

3. The growth in the housing stock for diﬀerent homeowner categories.

4. The rate at which homes turnover.

While each of these variables could in principal be obtained through a

dynamic programming solution technique, the stationarity and statistic mo-

ment concerns described above make it exceedingly challenging to solve for

these variables. Rather, we create scenarios based on knowledge of local char-

acteristics. For example, in localities with ample ﬁscal resources, the modeler

might reasonably expect that the government might build the infrastructure

necessary to support continued home price appreciation. Alternatively, if

the local economy is highly vulnerable to climate shocks, the degree of local

home price devaluation might be much higher as jobs disappear and indi-

viduals are no longer able to pay their mortgages. Yet another case is one

in which migration from vulnerable to non-vulnerable areas within the same

locality is desirable leads to falling home prices in the former and rising home

prices in the latter. Whether this last eﬀect dominates is an open question.

We therefore use scenarios to illustrate how the model can be used to

process scenarios, where the speciﬁc scenario assumptions are given in the

text. We describe the basis for some of these assumptions below.

G.1 Hurricane shock devaluation

For our scenarios, we assume that instantaneous devaluations due to a hurri-

cane shock only occur for hurricane categories 3 and above, with the degree of

devaluation rising in the strength of the hurricane. The highest devaluation

of 80 percent is based on a study by the McKinsey Global Institute.

G.2 Home price depreciation

We follow MGI’s projections and set the price trend for homes in ﬂoodplains

at a pace to bring them to a 30 percent devaluation relative to above-ﬂood-

plain homes by 2030, and an 80 percent devaluation relative to above-ﬂood-

plain homes by 2050.

Sustained home devaluation implies that negative

equity will set in at some point depending on the diﬀerence between the rate

of home price depreciation and the rate of mortgage interest. More generally,

if it is clear that prices will face sustained downward pressure, prices should

jump to the foreseen lower level immediately consistent with rational pricing

models. This is a diﬃcult issue to handle in light of the large empirical

literature attempting to explain coastal real estate pricing anomalies. We

leave proper consideration of coastal home pricing for future work.

For above-ﬂood-plain homes, let:

P I

2020

·10

= P I

2030

where P I

year

is a price index for a given year for above-ﬂood-plain homes of

type XA, and g

is the annual growth rate of those prices. Likewise:

P I

2020

·10

= P I

2030

If ﬂood plain homes are to depreciate 30 percent relative to above-ﬂood-plain

homes, it must be the case that:

P I

2020

·10

= 0.7 × P I

2030

Set P I

2020

= P I

2020

= 1. Combining this with the above equations, we

McKinsey Global Institute (2020) [25], p. 20.

McKinsey Global Institute (2020) [25], p. 20, maximum devaluation projections.

have the following growth rates for the period 2020-2030:

ln (0.7)

+ g

= g

− 0.036,

= 0.022 − 0.036 = −0.014.

where g

is set equal to 2.2 percent, equal to the best 30 year average growth

in the US housing market (1976-2005).

Performing the same exercise for the period 2030-2050, we have:

ln (0.7)

+ g

= g

− 0.080,

= 0.00 − 0.080 = −0.080.

where we assume prices for above-ﬂood-plain homes in Miami are ﬂat.

One consideration that sustained home devaluation introduces is that the

speed with which negative equity sets in depends on the diﬀerence between

the rate of home price depreciation and the rate of mortgage interest. More

generally, if it is clear that prices will face sustained downward pressure,

rational pricing models would generate an immediate jump to a lower price

consistent with the fundamentals. However, the empirical evidence on the

impact of climate change (particularly sea level rise) on home prices is mixed,

with some evidence that greater exposure to climate risk lowers home prices

but also evidence that risks are not fully capitalized. Moreover, homeowner

beliefs tend to aﬀect the degree to which climate risks are incorporated into

prices. Empirical studies of natural disasters suggest that prices tend to

rebound, and that homeowners who hold on long enough tend to be rewarded

with the recovery of any lost equity. There is always a real options value

to waiting to see how uncertainty is resolved before taking an irreversible

action. In light of these conﬂicting factors, we assume that homeowners do

not default outside of a hurricane event.

G.3 Housing unit growth

In some scenarios, the housing stock grows at the Miami population growth

rate during the 2020s (projected to be 1.16 percent according to the planning

horizon ﬁgures used by Miami-Dade County).

https://www.miamidade.gov/water/library/reports/reuse-feasibility-iii.pdf

G.4 Turnover rates

Bank vulnerability to climate shocks will depend signiﬁcantly on the cohort

structure of home ownership that in turn depends upon the rate of real

estate turnover. Consider the polar case in which turnover is zero, new home

construction is zero, and all home equity is fully owned by the homeowners.

Under such a scenario, banks and other non-homeowners do not hold risk.

It is reasonable to assume that turnover rates will fall in riskier areas as it

becomes diﬃcult to see in a sinking market. Add to this the possibility that

government programs buy out homeowners living in high risk areas, and the

probability of additional lending in these areas declines. There are no clear

empirical examples of which we are aware to guide us in our choice of the

path of turnover rates.

References

[1] Ariza, Mario Alejandro, 2020. Disposable City: Miami’s Future on the

Shores of Climate Catastrophe. New York: Bold Type Books.

[2] Auerbach, Alan, Yuriy Gorodnichenko, and Daniel Murphy, 2019. Local

Fiscal Multipliers and Fiscal Spillovers in the United States, NBER

Working Paper No. 25457. http://www.nber.org/papers/w25457.

[3] Bakkensen, Laura, and Lint Barrage. (2021) ”Flood Risk Belief Het-

erogeneity and Coastal Home Price Dynamics: Going Under Water?”

NBER Working Paper No. 23854.

[4] Baldauf, Markus, Lorenzo Garlappi, and Constantine Yannelis. (2020)

”Does Climate Change Aﬀect Real Estate Prices? Only If You Believe

In It,” The Review of Financial Studies, 33(3): 1256–95.

[5] Batten, Sandra, Rhiannon Sowerbutts, and Misa Tanaka, 2016. Let’s

talk about the weather: The impact of climate change on central banks,

Bank of England Working Paper No. 603.

[6] Bhutta, Neil, Jane Dokko, and Hui Shan, 2010. The Depth of Negative

Equity and Mortgage Default Decisions, Federal Reserve Board Finance

and Economics Discussion Series 2010–35.

[7] Bjarnadottir, Sigridur, Yue Li, and Mark G. Stewart, 2014. Regional

loss estimation due to hurricane wind and hurricane-induced surge con-

sidering climate variability, Structure and Infrastructure Engineering.

10 (11): 1369–1384. doi:10.1080/15732479.2013.816973.

[8] Congressional Budget Oﬃce, 2019. Expected Costs of Damage

From Hurricane Winds and Storm-Related Flooding. Available at:

www.cbo.gov/publication/55019.

[9] Dennis, Benjamin, 2022. Climate Change and Financial Policy: A Liter-

ature Review. Federal Reserve Board Finance and Economics Discussion

Series 2022–48.

[10] Elliot, Diana, Tanaya Srini, Shiva Kooragayala, and Carl Hedman, 2017.

Miami and the State of Low- and Middle-Income Housing: Strategies to

Preserve Aﬀordibility and Opportunties for the Future, Urban Institute

Research Report.

[11] Elliott, Diana, Tanaya Srini, Shiva Kooragayala, and Carl Hedman,

2017. Miami and the State of Low- and Middle-Income Housing: Strate-

gies to Preserve Aﬀordability and Opportunities for the Future, Urban

Institute Research Report.

[12] Emanuel, Kerry, and Thomas Jagger, 2010. On Estimating Hurricane

Return Periods, Journal of Applied Meteorology and Climatology. 49:

837–44. doi:10.1175/2009JAMC2236.1.

[13] Federal Emergency Management Agency. FEMA’s Hazus Program.

https//www.fema.gove/ﬂood-maps/products-tools/hazus

[14] Flavelle, Christopher. (2022a) “Why Ian May Push Florida Real Estate

Out of Reach for All but the Super Rich.” New York Times. October

13, 2022.

[15] Flavelle, Christopher. (2022b) “Hurricane Ian’s Toll Is Severe. Lack of

Insurance Will Make It Worse.” New York Times. September 29, 2022.

[16] Foote, Christopher, Kristopher Gerardi, and Paul Willen, 2008. Nega-

tive Equity and Foreclosure: Theory and Evidence, Journal of Urban

Economics. 64 (2): 234–45.

[17] Frame, W. Scott, 2010. Estimating the Eﬀect of Mortgage Foreclosures

on Nearby Property Values: A Critical Review of the Literature, Eco-

nomic Review, Federal Reserve Bank of Atlanta, No. 3.

[18] Genovese, Elisabetta, and Chloe Green, 2014. Assessment of Storm

Surge Damage to Coastal Settlements in Southeast Florida, Journal of

Risk Research. 18 (4): 407–27.

[19] Goldsmith-Pinkham, Paul, Matthew Gustafson, and Ryan Lewis, 2019.

Sea level rise and municipal bond yields, Jacobs Levy Equity Manage-

ment Center for Quantitative Financial Research Paper. Available at:

http://dx.doi.org/10.2139/ssrn.3478364.

[20] Goodell, Jeﬀ, 2017. The Water Will Come: Rising Seas, Sinking Cities,

and the Remaking of the Civilized World. New York: Little, Brown and

Company.

[21] Keenan, Jesse, and Jacob T. Bradt, 2020. Underwaterwriting: from

theory to empiricism in regional mortgage markets in the U.S., Climatic

Change, 162: 2043–67.

[22] Keenan, Jesse, Thomas Hill, and Anurag Gumber, 2018. Climate Gen-

triﬁcation: from Theory to Empiricism in Miami-Dade County, Florida,

Environmental Research Letters. 13. doi:10.1088/1748-9326/aabb32.

[23] Huang, Z., D.V. Rosowsky, and P.R. Sparks, 2001. Long-term Hurri-

cane Risk Assessment and Expected Damage to Residential Structures.

Reliability Engineering and System Safety. 74: 239–249.

[24] McAlpine, Steven, Jeremy Porter, 2018. Estimating Recent Local Im-

pacts of Sea-Level Rise on Current Real-Estate Losses: A Housing Mar-

ket Case Study in Miami-Dade, Florida, Population Research and Policy

Review. 37: 871–95. doi:10.1007/s11113-018-9473-5.

[25] McKinsey Global Insitute, 2020. Will mortgages and mar-

kets stay aﬂoat in Florida?. MGI Climate risk and re-

sponse: Physical hazards and socioeconomic impacts

Case Study. Available at:https://www.mckinsey.com/ /me-

dia/mckinsey/business%20functions/ sustainabil-

ity/our%20insights/will%20mortgages%20and%20markets%20stay

%20aﬂoat%20in%20ﬂorida/mgi-will-mortgages-and-markets-stay-

aﬂoat-in-ﬂorida.pdf.

[26] Montero Kuscevic, Casto Mart´ın, 2014. Okun’s law and urban spillovers

in US unemployment, The Annals of Regional Science. 53 (3): 719–730.

[27] Ouazad, Amine, and Matthew Kahn, 2021. Mortgage ﬁnance and cli-

mate change: Securitization dynamics in the aftermath of natural dis-

asters, NBER Working Paper 26322, Doi: 10.3386/w26322.

[28] Painter, M., 2020. An inconvenient cost: The eﬀects of climate change

on municipal bonds, Journal of Financial Economics. 135 (2): 468–82.

doi:10.1016/j.jﬁneco.2019.06.006.

[29] Pennington-Cross, Anthony, 2006. The Value of Foreclosed Property.

Journal of Real Estate Research. 28 (2): 193–214.

[30] Pindyck, Robert. 2021. What We Know and Don’t Know about Climate

Change, and the Implications for Policy, in Matthew Kotchen, James

H. Stock & Catherine Wolfram (eds.) Environmental and Energy Policy

and the Economy, Volume 2, Chicago: University of Chicago Press.

[31] Pozsar, Zoltan. 2014. Shadow banking: The money view, OFR Working

Paper 14-04. Oﬃce of Financial Research.

[32] Rosoﬀ, Stephanie, and Jessica Yager, 2017. Housing in the U.S. Flood-

plains, NYU Furman Center Data Brief.

[33] Rozsa, Lori, and Erica Werner. (2022) “Florida’s insurance woes could

make Ian’s economic wrath even worse,” Washington Post, September

30, 2022.

https://www.washingtonpost.com/climate-

environment/2022/09/30/ian-ﬂorida-economy-insurance/

[34] Scharlemann, Therese, and Stephen Shore, 2016. The Eﬀect of Negative

Equity on Mortgage Default: Evidence from HAMP’s Principal Reduc-

tion Alternative, The Review of Financial Studies. 29 (10): 2850–83.

doi:10.1093/rfs/hhw034.

[35] Sullivan Sealey, Kathleen, Ray King Burch, P.-M. Binder, 2018. Will

Miami Survive?: The Dynamic Interplay Between Floods and Finance.

Springer Briefs in Geography, doi:10.1007/978-3-319-79020-6.

[36] Union of Concerned Scientists, 2016. Encroaching Tides

in Miami-Dade County, Florida: Investing in Prepared-

ness to Manage the Impacts of Rising Seas. Available at:

www.ucsusa.org/EncroachingTidesMiamiDade.

[37] Weitzman, Martin. 2011. Fat-tailed uncertainty in the eco-

nomics of catastrophic climate change, Review of Environ-

mental Economics and Policy, 5(2): 275–92. Available at:

http://reep.oxfordjournals.org/content/5/2/275